SoroushVahidi/scidocs huggingface dataset:
Combining geometry and combinatorics: A unified approach to sparse signal recovery
(2008)

There are two main algorithmic approaches to sparse signal recovery: geometric and combinatorial. The geometric approach utilizes geometric properties of the measurement matrix Phi. A notable example is the Restricted Isometry Property, which states that the mapping Phi preserves the Euclidean norm of sparse signals; it is known that random dense matrices satisfy this constraint with high probability. On the other hand, the combinatorial approach utilizes sparse matrices, interpreted as adjacency matrices of sparse (possibly random) graphs, and uses combinatorial techniques to recover an approximation to the signal. In this paper we present a unification of these two approaches. To this end, we extend the notion of Restricted Isometry Property from the Euclidean lscr2 norm to the Manhattan lscr1 norm. Then we show that this new lscr1 -based property is essentially equivalent to the combinatorial notion of expansion of the sparse graph underlying the measurement matrix. At the same time we show that the new property suffices to guarantee correctness of both geometric and combinatorial recovery algorithms. As a result, we obtain new measurement matrix constructions and algorithms for signal recovery which, compared to previous algorithms, are superior in either the number of measurements or computational efficiency of decoders.
Chapter 3 Semantic Web Service Description
(2008)

The convergence of semantic Web with service oriented computing is manifested by Semantic Web Services (SWS) technology. It addresses the major challenge of automated, interoperable and meaningful coordination of Web Services to be carried out by intelligent software agents. In this chapter, we briefly discuss prominent SWS description frameworks, that are the standard SAWSDL, OWL-S and WSML. This is complemented by main critics of Semantic Web Services, and selected references to further readings on the subject.
Reconstructing Storyline Graphs for Image Recommendation from Web Community Photos
(2014)

In this paper, we investigate an approach for reconstructing storyline graphs from large-scale collections of Internet images, and optionally other side information such as friendship graphs. The storyline graphs can be an effective summary that visualizes various branching narrative structure of events or activities recurring across the input photo sets of a topic class. In order to explore further the usefulness of the storyline graphs, we leverage them to perform the image sequential prediction tasks, from which photo recommendation applications can benefit. We formulate the storyline reconstruction problem as an inference of sparse time-varying directed graphs, and develop an optimization algorithm that successfully addresses a number of key challenges of Web-scale problems, including global optimality, linear complexity, and easy parallelization. With experiments on more than 3.3 millions of images of 24 classes and user studies via Amazon Mechanical Turk, we show that the proposed algorithm improves other candidate methods for both storyline reconstruction and image prediction tasks.
A note on multi-image denoising
(2009)

Taking photographs under low light conditions with a hand-held camera is problematic. A long exposure time can cause motion blur due to the camera shaking and a short exposure time gives a noisy image. We consider the new technical possibility offered by cameras that take image bursts. Each image of the burst is sharp but noisy. In this preliminary investigation, we explore a strategy to efficiently denoise multi-images or video. The proposed algorithm is a complex image processing chain involving accurate registration, video equalization, noise estimation and the use of state-of-the-art denoising methods. Yet, we show that this complex chain may become risk free thanks to a key feature: the noise model can be estimated accurately from the image burst. Preliminary tests will be presented. On the technical side, the method can already be used to estimate a non parametric camera noise model from any image burst.
Air-ground localization and map augmentation using monocular dense reconstruction
(2013)

We propose a new method for the localization of a Micro Aerial Vehicle (MAV) with respect to a ground robot. We solve the problem of registering the 3D maps computed by the robots using different sensors: a dense 3D reconstruction from the MAV monocular camera is aligned with the map computed from the depth sensor on the ground robot. Once aligned, the dense reconstruction from the MAV is used to augment the map computed by the ground robot, by extending it with the information conveyed by the aerial views. The overall approach is novel, as it builds on recent developments in live dense reconstruction from moving cameras to address the problem of air-ground localization. The core of our contribution is constituted by a novel algorithm integrating dense reconstructions from monocular views, Monte Carlo localization, and an iterative pose refinement. In spite of the radically different vantage points from which the maps are acquired, the proposed method achieves high accuracy whereas appearance-based, state-of-the-art approaches fail. Experimental validation in indoor and outdoor scenarios reported an accuracy in position estimation of 0.08 meters and real time performance. This demonstrates that our new approach effectively overcomes the limitations imposed by the difference in sensors and vantage points that negatively affect previous techniques relying on matching visual features.
Radar cross section measurement with 77 GHz automotive FMCW radar
(2016)

In this paper, radar cross section (RCS) measurement for human subjects and vehicles in 77 GHz automotive frequency modulated continuous wave (FMCW) radar system is presented. In this system, it is impossible to utilize conventional RCS definition due to high frequency band and the modulation technique used. Therefore, we introduce a new parameter called pseudo-RCS that can replace the conventional RCS. Then, we conduct actual experiment in the road to measure the RCS of the human subjects and the vehicles. In the measurement, data of four human subjects and four different kinds of vehicles are recorded with the 77 GHz FMCW radar. From the actual measured data, we find RCS distributions of the human subjects and the vehicles. For the human subjects, the RCS values are distributed following the Nakagami distribution. On the other hand, the log-normal distribution is well fit for the RCS values in the case of the vehicle.
Treatment of molluscum contagiosum with cantharidin: a practical approach.
(2010)

Molluscum contagiosum is very common. In this article we discuss the use of cantharidin as a treatment option for molluscum contagiosum and give detailed information about distribution sources, how to apply it, and caveats regarding its use.Molluscum contagiosum is a common viral disease of childhood caused by a poxvirus, which presents with small, firm, dome-shaped, umbilicated papules. It is generally benign and self-limited, with spontaneous resolution within 6 months to several years. Watchful waiting can often be an appropriate management strategy; however, some patients either desire or require treatment. Reasons for actively treating molluscum contagiosum may include alleviation of discomfort and itching (particularly in patients where an eczematous eruption - the so-called "molluscum eczema" - is seen in association) or in patients with ongoing atopic dermatitis where more lesions are likely to be present. Other reasons for treatment include limitation of spread to other areas and people, prevention of scarring and superinfection, and elimination of the social stigma of visible lesions. No one treatment is uniformly effective.Treatment options include destructive therapies (curettage, cryotherapy, cantharidin, and keratolytics, among others), immunomodulators (imiquimod, cimetidine, and Candida antigen), and antivirals (cidofovir). In this article we discuss and describe our first-line treatment approach for those molluscum needing treatment - cantharidin.
Telemanipulation of Snake-Like Robots for Minimally Invasive Surgery of the Upper Airway
(2006)

Abstract. This research focuses on developing and testing the highlevel control of a novel eight DOF hybrid robot using a DaVinci master manipulator. The teleoperation control is formulated as weighted, multi objective constrained least square (LS) optimization problems one for the master controller and the other for the slave controller. This allows us to incorporate various virtual fixtures in our control algorithm as constraints of the LS problem based on the robot environment. Experimental validation to ensure position tracking and sufficient dexterity to perform suturing in confined spaces such as throat are presented.
Start Later, Sleep Later: School Start Times and Adolescent Sleep in Homeschool Versus Public/Private School Students.
(2016)

Homeschooled students provide a naturalistic comparison group for later/flexible school start times. This study compared sleep patterns and sleep hygiene for homeschooled students and public/private school students (grades 6-12). Public/private school students (n = 245) and homeschooled students (n = 162) completed a survey about sleep patterns and sleep hygiene. Significant school group differences were found for weekday bedtime, wake time, and total sleep time, with homeschooled students waking later and obtaining more sleep. Homeschooled students had later school start times, waking at the same time that public/private school students were starting school. Public/private school students had poorer sleep hygiene practices, reporting more homework and use of technology in the hour before bed. Regardless of school type, technology in the bedroom was associated with shorter sleep duration. Later school start times may be a potential countermeasure for insufficient sleep in adolescents. Future studies should further examine the relationship between school start times and daytime outcomes, including academic performance, mood, and health.
Robot navigation: Review of techniques and research challenges
(2016)

Robot navigation has multi-spectral applications across a plethora of industries. Being one of the fastest evolving field of Artificial intelligence, it has garnered a lot of speculation and the problem of efficient Robot navigation has become a talking point in the “Artificial Intelligence research community”. Robot Navigation can be thought of as a collection of sub-problems that when solved efficiently will (most likely) give the complete and the most efficient solution. While the main Navigation problem can be divided into numerous smaller ones some of the prevalent (sub)-tasks associated with Robot Navigation are Path Planning, Collision Prevention, Search Algorithm and an appropriate pictorial (graphical) representation of the given environment. Path Planning gives us the most optimal path finding approach while keeping the dynamism of the environment and the objects in mind. Collision Prevention ensures the Robot reaches its goal without colliding with any of the objects present in the environment. Collision Prevention techniques' vitality increases considerably when the number of objects in the input space is large (>10, approximately). Search Algorithms are the functional units of this problem as they determine the path the Robot follows while traveling to its destination. Since the environment greatly affects the choice of technique for all the other tasks it is vital to have a clear, concise and a non-ambiguous representation of the same. The main aim of the paper is to summarise the development of various techniques in the multiple domains of the Robot Navigation problem in the last fifteen years (2000-2015) while stating the scope and the restriction of each of them.
Unsupervised Pattern Discovery in Speech
(2008)

We present a novel approach to speech processing based on the principle of pattern discovery. Our work represents a departure from traditional models of speech recognition, where the end goal is to classify speech into categories defined by a prespecified inventory of lexical units (i.e., phones or words). Instead, we attempt to discover such an inventory in an unsupervised manner by exploiting the structure of repeating patterns within the speech signal. We show how pattern discovery can be used to automatically acquire lexical entities directly from an untranscribed audio stream. Our approach to unsupervised word acquisition utilizes a segmental variant of a widely used dynamic programming technique, which allows us to find matching acoustic patterns between spoken utterances. By aggregating information about these matching patterns across audio streams, we demonstrate how to group similar acoustic sequences together to form clusters corresponding to lexical entities such as words and short multiword phrases. On a corpus of academic lecture material, we demonstrate that clusters found using this technique exhibit high purity and that many of the corresponding lexical identities are relevant to the underlying audio stream.
Hypothalamic Orexin Neurons Regulate Arousal According to Energy Balance in Mice
(2003)

Mammals respond to reduced food availability by becoming more wakeful and active, yet the central pathways regulating arousal and instinctual motor programs (such as food seeking) according to homeostatic need are not well understood. We demonstrate that hypothalamic orexin neurons monitor indicators of energy balance and mediate adaptive augmentation of arousal in response to fasting. Activity of isolated orexin neurons is inhibited by glucose and leptin and stimulated by ghrelin. Orexin expression of normal and ob/ob mice correlates negatively with changes in blood glucose, leptin, and food intake. Transgenic mice, in which orexin neurons are ablated, fail to respond to fasting with increased wakefulness and activity. These findings indicate that orexin neurons provide a crucial link between energy balance and arousal.
Design of wideband printed rectangular monopole patch antenna with band notch
(2018)

In this paper, a microstrip-line fed printed monopole rectangular microstrip patch antenna with wide bandwidth having frequency band-notch function for wireless communication have been proposed. Bandwidth enhancement is obtained by rounding the corners of rectangular patch and modifying the ground plane. By cutting rectangular slot on the radiating element, the frequency band-notch characteristic is obtained. FR-4 substrate having dielectric constant of 4.4 and thickness of 1.6 mm is used to simulate printed rectangular monopole antenna (PRMA) having an overall size of substrate 55 × 45 mm2. The designed antenna is simulated using CAD FEKO simulation software. The proposed antenna has impedance bandwidth of 6.61 GHz (2.11 GHz to 8.72 GHz) with a rejection band from 3.03 GHz to 3.48 GHz. The observed gain of proposed antenna is 2.42 dBi. The proposed microstrip patch antenna is fabricated, tested and measured result is presented in this paper.
Co-Clustering with Side Information for Text mining
(2016)

Many of the text mining applications contain a huge amount of information from document in the form of text. This text can be very helpful for Text Clustering. This text also includes various kind of other information known as Side Information or Metadata. Examples of this side information include links to other web pages, title of the document, author name or date of Publication which are present in the text document. Such metadata may possess a lot of information for the clustering purposes. But this Side information may be sometimes noisy. Using such Side Information for producing clusters without filtering it, can result to bad quality of Clusters. So we use an efficient Feature Selection method to perform the mining process to select that Side Information which is useful for Clustering so as to maximize the advantages from using it. The proposed technique, CCSI (Co-Clustering with Side Information) system makes use of the process of Co-Clustering or Two-mode clustering which is a data mining technique that allows concurrently clustering of the rows and columns of a matrix.
Gaze tracking by Binocular Vision and LBP features
(2008)

In this paper, a new method for eye gaze tracking is proposed under natural head movement. In this method, Local-Binary-Pattern Texture Feature (LBP) is adopted to calculate the eye features according to the characteristic of the eye, and a precise Binocular Vision approach is used to detect the space coordinate of the eye. The combined features of space coordinates and LBP features of the eyes are fed into Support Vector Regression (SVR) to match the gaze mapping function, in the hope of tracking gaze direction under natural head movement. The experimental results prove that the proposed method can determine the gaze direction accurately.
Bridging the Gap: From Research to Practical Advice
(2018)

Software engineers must solve practical problems under deadline pressure. They rely on the best-codified knowledge available, turning to weaker results and their expert judgment when sound science is unavailable. Meanwhile, software engineering researchers seek fully validated results, resulting in a lag to practical guidance. To bridge this gap, research results should be systematically distilled into actionable guidance in a way that respects differences in strength and scope among the results. Starting with the practitioners’ need for actionable guidance, this article reviews the evolution of software engineering research expectations, identifies types of results and their strengths, and draws on evidence-based medicine for a concrete example of deriving pragmatic guidance from mixed-strength research results. It advances a levels-of-evidence framework to allow researchers to clearly identify the strengths of their claims and the supporting evidence for their results and to work with practitioners to synthesize actionable recommendations from diverse types of evidence. This article is part of a special issue on software engineering’s 50th anniversary.
Why did TD-Gammon Work?
(1996)

Although TD-Gammon is one of the major successes in machine learning, it has not led to similar impressive breakthroughs in temporal difference learning for other applications or even other games. We were able to replicate some of the success of TD-Gammon, developing a competitive evaluation function on a 4000 parameter feed-forward neural network, without using back-propagation, reinforcement or temporal difference learning methods. Instead we apply simple hill-climbing in a relative fitness environment. These results and further analysis suggest that the surprising success of Tesauro’s program had more to do with the co-evolutionary structure of the learning task and the dynamics of the backgammon game itself.
Subdivision templates for converting a non-conformal hex-dominant mesh to a conformal hex-dominant mesh without pyramid elements
(2008)

This paper presents a computational method for converting a non-conformal hex-dominant mesh to a conformal hex-dominant mesh without help of pyramid elements. During the conversion, the proposed method subdivides a non-conformal element by applying a subdivision template and conformal elements by a conventional subdivision scheme. Although many finite element solvers accept mixed elements, some of them require a mesh to be conformal without a pyramid element. None of the published automated methods could create a conformal hex-dominant mesh without help of pyramid elements, and therefore the applicability of the hex-dominant mesh has been significantly limited. The proposed method takes a non-conformal hex-dominant mesh as an input and converts it to a conformal hex-dominant mesh that consists only of hex, tet, and prism elements. No pyramid element will be introduced. The conversion thus considerably increases the applicability of the hex-dominant mesh in many finite element solvers.
Total parser combinators
(2010)

A monadic parser combinator library which guarantees termination of parsing, while still allowing many forms of left recursion, is described. The library's interface is similar to those of many other parser combinator libraries, with two important differences: one is that the interface clearly specifies which parts of the constructed parsers may be infinite, and which parts have to be finite, using dependent types and a combination of induction and coinduction; and the other is that the parser type is unusually informative.
 The library comes with a formal semantics, using which it is proved that the parser combinators are as expressive as possible. The implementation is supported by a machine-checked correctness proof.
An Inside Look at Botnets
(2007)

The continued growth and diversification of the Internet has been accompanied by an increasing prevalence of attacks and intrusions [40]. It can be argued, however, that a significant change in motivation for malicious activi ty has taken place over the past several years: from vandalism and recognition in th e hacker community, to attacks and intrusions for financial gain. This shift has bee n marked by a growing sophistication in the tools and methods used to conduct atta cks, thereby escalating the network security arms race. Our thesis is that the reactivemethods for network security that are predominant today are ultimately insufficient and that more proactivemethods are required. One such approach is to develop a foundational understanding of the mechanisms employed by malicious software (malware) which is often readi ly available in source form on the Internet. While it is well known that large IT secu rity companies maintain detailed databases of this information, these are not o penly available and we are not aware of any such open repository. In this paper we begin t he process of codifying the capabilities of malware by dissecting four widely-u sed Internet Relay Chat (IRC) botnet codebases. Each codebase is classified along se ven k y dimensions including botnet control mechanisms, host control mechani sms, propagation mechanisms, exploits, delivery mechanisms, obfuscation and de ception mechanisms. Our study reveals the complexity of botnet software, and we disc us es implications for defense strategies based on our analysis.
Slow and Steady Feature Analysis: Higher Order Temporal Coherence in Video
(2016)

How can unlabeled video augment visual learning? Existing methods perform "slow" feature analysis, encouraging the representations of temporally close frames to exhibit only small differences. While this standard approach captures the fact that high-level visual signals change slowly over time, it fails to capture how the visual content changes. We propose to generalize slow feature analysis to "steady" feature analysis. The key idea is to impose a prior that higher order derivatives in the learned feature space must be small. To this end, we train a convolutional neural network with a regularizer on tuples of sequential frames from unlabeled video. It encourages feature changes over time to be smooth, i.e., similar to the most recent changes. Using five diverse datasets, including unlabeled YouTube and KITTI videos, we demonstrate our method's impact on object, scene, and action recognition tasks. We further show that our features learned from unlabeled video can even surpass a standard heavily supervised pretraining approach.